1. Field of the Invention
The present invention relates to a dereverberation apparatus and a dereverberation method.
2. Description of the Related Art
A reverberation reducing process is an important technique used to pre-process auto-speech recognition, aiming at improvement of articulation in a teleconference call or a hearing aid and improvement of a recognition rate of auto-speech recognition used for speech recognition of a robot (robot hearing sense) (see, for example, Japanese Unexamined Patent Application, First Publication No. H09-261133). In the related art, there has been proposed a reverberation reducing process based on a Multiple-input/output INverse-filtering Theorem (MINT) which is theoretically capable of dereverberation with high precision without nonlinear distortion (see, for example, M. MIYOSHI and Y. KANEDA, “Inverse filtering of room acoustics,” IEEE Transactions on Speech and Audio Processing, Vol. 36, No. 2, pp. 145-152, 1988). The reverberation reducing process for the auto-speech recognition of the robot hearing sense needs to satisfy three conditions, i.e., no pre-measurement of acoustic transfer characteristics (blind), real-time processability and no nonlinear distortion by the process.
Examples of methods to satisfy these three conditions may include a Semi-Blind-MINT (SBM) (see, for example, FURUYA Kenichi and KATAOKA Akitoshi, “Semi-blind dereverberation using an interchannel correlation matrix and a whitening filter,” Technology Research Report of The Institute of Electronics, Information and Communication Engineers (IEICE), Vol. J88-A, No. 10, pp. 1089-1099, 2005), which is a dereverberation method based on MINT, and a Decorrelation-based Adaptive Inverse Filtering (DAIF) (see, for example, NAKAJIMA Hirofumi, NAKADAI Kazuhiro, HASEGAWA Yuuji and TSUJINO Hiroshi, “Blind dereverberation using decorrelation-based adaptive inverse filtering,” Journal (Autumn) of Acoustical Society of Japan (ASJ), pp. 713-714, 2008).
SBM is an extended MINT which requires no pre-measurement of an acoustic transfer function from a sound source to a microphone (blind process), and can perform a reverberation reducing process with high precision only using a recorded signal. SBM is particularly effective for environments with few changes in the positions of microphones or sound sources, such as teleconference calls. However, since SBM computes filters in blocks of units, it requires time for adaptation, which makes it difficult to be used for applications where the positions of microphones or sound sources are greatly varied, such as auto-speech recognition in the robot hearing sense.
DAIF has been suggested to overcome such a problem of SBM. DAIF has high-speed adaptability since it performs a process in a sample-by-sample manner. However, since it updates coefficients based on an instantaneous correlation matrix, many errors occur in updating the coefficients, which leads to deterioration of performance of dereverberation process.
In prior dereverberation processes such as SBM and DAIF, since more channels generally provide a higher performance of dereverberation process, all available channels were used for the dereverberation process.
SBM and DAIF, which are common dereverberation methods, have a presumption that an initial arrival channel is known. When this presumption is not satisfied, noticeable deterioration of the dereverberation performance occurs as a result. If the position of a sound source can be limited to a defined range, such as in a teleconference call, an initial arrival channel can be known by means of the position of microphones.